Screening for functional circular RNAs using the CRISPR-Cas13 system

Li, Siqi; Li, Xiang; Xue, Wei; Zhang, Lin; Cao, Shi-Meng; Lei, Yun-Ni; Yang, Liang-Zhong; Cao, Si-Kun; Zhang, Jialin; Gao, Xiang; Wei, Jia; Li, Jinsong; Yang, Li

doi:10.1101/2020.03.23.002865

Cited by 29 publications

(41 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, the novel CRISPR/Cas13-based approaches using guide RNAs, which target the circRNA-specific back-splicing junction sites, have been used to achieve precise and robust circRNA silencing without disturbing their linear cognate mRNAs [67,68]. These studies suggest that the application of CRISPR/Cas-based circRNA engineering systems may be a promising method for performing functional studies of circRNA in human cancers.…”

Section: Methods For Detecting and Characterizing Circrnasmentioning

confidence: 99%

The Expression, Functions and Mechanisms of Circular RNAs in Gynecological Cancers

Dong

Xiong

et al. 2020

Cancers

View full text Add to dashboard Cite

Circular RNAs (circRNAs) are covalently closed, endogenous non-coding RNAs and certain circRNAs are linked to human tumors. Owing to their circular form, circRNAs are protected from degradation by exonucleases, and therefore, they are more stable than linear RNAs. Many circRNAs have been shown to sponge microRNAs, interact with RNA-binding proteins, regulate gene transcription, and be translated into proteins. Mounting evidence suggests that circRNAs are dysregulated in cancer tissues and can mediate various signaling pathways, thus affecting tumorigenesis, metastasis, and remodeling of the tumor microenvironment. First, we review the characteristics, biogenesis, and biological functions of circRNAs, and describe various mechanistic models of circRNAs. Then, we provide a systematic overview of the functional roles of circRNAs in gynecological cancers. Finally, we describe the potential future applications of circRNAs as biomarkers for prognostic stratification and as therapeutic targets in gynecological cancers. Although the function of most circRNAs remains elusive, some individual circRNAs have biologically relevant functions in cervical cancer, ovarian cancer, and endometrial cancer. Certain circRNAs have the potential to serve as biomarkers and therapeutic targets in gynecological cancers.

show abstract

Section: Methods For Detecting and Characterizing Circrnasmentioning

confidence: 99%

The Expression, Functions and Mechanisms of Circular RNAs in Gynecological Cancers

Dong

Xiong

et al. 2020

Cancers

View full text Add to dashboard Cite

show abstract

“…The Cas13 family is unified by the presence of two conserved HEPN ribonuclease motifs and a common RNA cleavage mechanism, yet divided into several subtypes on the basis of sequence diversity and coding sequence length. Cas13d enzymes, in particular the engineered Cas13d-NLS from R. flavefaciens strain XPD3002 (CasRx) (Konermann et al, 2018), are highly compact RNA targeting effectors with robust activity in mammalian and plant cells relative to other subtypes (Wessels et al, 2020;Li et al, 2021;Mahas et al, 2019), motivating their further development as RNA targeting tools.…”

Section: Introductionmentioning

confidence: 99%

Deep learning and CRISPR-Cas13d ortholog discovery for optimized RNA targeting

Wei

Lotfy

Faizi

et al. 2021

Preprint

View full text Add to dashboard Cite

Transcriptome engineering requires flexible RNA-targeting technologies that can perturb mammalian transcripts in a robust and scalable manner. CRISPR systems that natively target RNA molecules, such as Cas13 enzymes, are enabling rapid progress in the investigation of RNA biology and advancement of RNA therapeutics. Here, we sought to develop a Cas13 platform for high-throughput phenotypic screening and elucidate the design principles underpinning its RNA targeting efficiency. We employed the RfxCas13d (CasRx) system in a positive selection screen by tiling 55 known essential genes with single nucleotide resolution. Leveraging this dataset of over 127,000 guide RNAs, we systematically compared a series of linear regression and machine learning algorithms to train a convolutional neural network (CNN) model that is able to robustly predict guide RNA performance based on guide sequence alone. We further incorporated secondary features including secondary structure, free energy, target site position, and target isoform percent. To evaluate model performance, we conducted orthogonal screens via cell surface protein knockdown. The final CNN model is able to predict highly effective guide RNAs (gRNAs) within each transcript with >90% accuracy in this independent test set. To provide user interpretability, we evaluate feature contributions using both integrated gradients and SHapley Additive exPlanations (SHAP). We identify a specific sequence motif at guide position 15-24 along with selected secondary features to be predictive of highly efficient guides. Taken together, we derive Cas13d guide design rules from large-scale screen data, release a guide design tool (http://rnatargeting.org) to advance the RNA targeting toolbox, and describe a path for systematic development of deep learning models to predict CRISPR activity.

show abstract

“…In all cases, shRNAs, siRNAs, or gRNAs were designed to target sequences of circRNAfeatured BSJ sites for targeted circRNA repression. Comparative analyses suggested that circRNA knockdown by RfxCas13 showed much less off-target on cognate mRNAs than those by shRNAs/siRNAs [56]. Although nearly all BSJs are targetable by RfxCas13-gRNA, the execution at the RNA level of circRNA LOF depends on the continuous expression of the RfxCas13 system in cells.…”

Section: Discussionmentioning

confidence: 97%

“…5), in human 293FT and HCT116 cell lines. Other than KO, reducing circRNA expression was also reported at the RNA level by short hairpin RNAs (shRNAs), small interfering RNAs [26,27], or RNA-targeting type VI CRISPR effector RfxCas13 systems [56]. In all cases, shRNAs, siRNAs, or gRNAs were designed to target sequences of circRNAfeatured BSJ sites for targeted circRNA repression.…”

Section: Discussionmentioning

confidence: 99%

Knockout of circRNAs by base editing back-splice sites of circularized exons

Gao

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

A large number of circular RNAs (circRNAs) are produced from back-splicing of exon(s) of precursor mRNAs and generally co-expressed with their cognate linear RNAs from the same gene loci. Methods for circRNA-specific knockout are lacking, largely due to complete sequence-overlaps between circular and cognate linear RNAs. Here, we report to use base editors (BEs) for circRNA depletion. By targeting splice sites involved in both back-splicing and canonical splicing, BEs can repress both circular and linear RNAs expression, which confirms the requirement of canonical splice site signals for back-splice. Importantly, by targeting back-splice sites predominantly for circRNA biogenesis, BEs could efficiently repress the production of circular, but not linear cognate RNAs. As hundreds of exons were found to be predominantly back-spliced to produce circRNAs, this study provides an efficient method to deplete circRNAs for function study.

show abstract

Screening for functional circular RNAs using the CRISPR-Cas13 system

Cited by 29 publications

References 44 publications

The Expression, Functions and Mechanisms of Circular RNAs in Gynecological Cancers

The Expression, Functions and Mechanisms of Circular RNAs in Gynecological Cancers

Deep learning and CRISPR-Cas13d ortholog discovery for optimized RNA targeting

Knockout of circRNAs by base editing back-splice sites of circularized exons

Contact Info

Product

Resources

About